Hey, everyone, and welcome back to TIME series analysis.
So this lecture is the intro to the next section of the course, this section is going to be about how
to apply machine learning models, two time series.
Now, here's where the lines blur a little bit.
Note that what we studied earlier, Arima, is technically no different from the machine learning models.
We will study here.
In fact, one of the models we will study is the AP model we looked at previously.
So please keep this in mind that what is and what is not machine learning is just a subjective opinion.
At the end of the day, if you understand how these things work, what you call it is irrelevant.
Now, that being said, this section is more focused on models that would traditionally be taught in
a machine learning course.
Specifically, I've decided to focus on linear models, support vector machines and the random forest.
So the main idea of this section is that you're not going to learn any machine learning model depth.
This, of course, is about application two time series not deriving the duality theorem of support
vector machines, nor understanding the statistics behind why the random force works.
So to that end, the most important thing to focus on in this section is not any specific machine learning
model.
In fact, practically no discussion about any machine learning model is really necessary.
What you should focus on in this section are the kinds of tasks that machine learning can solve and
how we transform these tasks into time series tasks.
So this process is completely model agnostic.
That is, it doesn't really matter which machine learning models you use because the same rules apply
and because it doesn't matter which machine learning models you use.
Any discussion about a specific machine learning model is technically not needed.
Now, that being said, I know that there will be at least a few students who would like to know a little
something about the models will be using.
So they will be discussed in a more intuition only setting.
And again, if you want to learn these models in depth, you can always check the resources I've left
in extra reading that text.
So the outline for this section is pretty simple.
We'll start with the two main categories of supervised learning, classification and regression.
The important part of this is to understand these from a geometrical point of view.
You'll learn one of my most important rules.
Machine learning is nothing but geometry.
Once you understand these concepts, well, then look at how to transform them into auto regressive
models.
These are somewhat like the AP models you studied earlier, but more powerful.
The next step will be to quickly go over the intuition behind each machine learning model we will use.
Again, this isn't necessary, but it should at least give you some intuition behind the libraries you'll
be using.
One important topic of this section is extrapolation.
This is kind of another take on the concept of stationary, which we've looked at before.
The reason this is important is people often have this unrealistic view that machine learning is magic.
It can automatically learn any pattern from data.
Well, what we will learn in this section is that there is a limit to this belief.
This will further drive the point home that machine learning isn't magic, it's just geometry.
After this important lesson, we'll be ready to apply what we've learned to several time series data
sets.
OK, so I hope you're excited for this section.
Thanks for listening and I'll see you in the next lecture.